A functional regression model for heterogeneous BioGeoChemical Argo data in the Southern Ocean

TL;DR

This paper introduces a scalable functional regression model for analyzing sparse, heterogeneous Argo biogeochemical data in the Southern Ocean, integrating spatial dependence and mixture components to improve understanding and prediction of oceanic variables.

Contribution

The study develops a novel functional regression framework that models joint dependence of oxygen, temperature, and salinity, incorporating spatial mixture components for better front detection and data prediction.

Findings

Effective in cross-validation tests

Improves location estimates of ocean fronts

Provides comprehensive interpretation of joint data

Abstract

Leveraging available measurements of our environment can help us understand complex processes. One example is Argo Biogeochemical data, which aims to collect measurements of oxygen, nitrate, pH, and other variables at varying depths in the ocean. We focus on the oxygen data in the Southern Ocean, which has implications for ocean biology and the Earth's carbon cycle. Systematic monitoring of such data has only recently begun to be established, and the data is sparse. In contrast, Argo measurements of temperature and salinity are much more abundant. In this work, we introduce and estimate a functional regression model describing dependence in oxygen, temperature, and salinity data at all depths covered by the Argo data simultaneously. Our model elucidates important aspects of the joint distribution of temperature, salinity, and oxygen. Due to fronts that establish distinct spatial zones in the Southern Ocean, we augment this functional regression model with a mixture component. By modelling spatial dependence in the mixture component and in the data itself, we provide predictions onto a grid and improve location estimates of fronts. Our approach is scalable to the size of the Argo data, and we demonstrate its success in cross-validation and a comprehensive interpretation of the model.